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Magnus Hedemark
48a67bb0a1
Greenfield SkillOpt: 3 epochs for CrewAI skill. Role/Goal/Backstory agent model, sequential/hierarchical processes. 11 files: SKILL.md, 6 references, 3 templates, 1 script.
54 lines
1.4 KiB
Markdown
54 lines
1.4 KiB
Markdown
# CrewAI Tool Integration
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## @tool Decorator
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```python
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from crewai.tools import tool
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@tool("search_web")
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def search_web(query: str) -> str:
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"""Search the web for current information."""
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return f"Results for: {query}"
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@tool("calculate")
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def calculate(expression: str) -> str:
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"""Evaluate a mathematical expression."""
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return str(eval(expression))
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```
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## Assigning Tools
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Tools are assigned to agents:
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```python
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agent = Agent(
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role="Researcher",
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goal="Find information",
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backstory="Expert researcher",
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tools=[search_web, calculate], # Agent-level tools
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)
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# Or task-level (overrides agent tools)
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task = Task(
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description="Research and compute",
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agent=agent,
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tools=[search_web], # Task-specific — only this tool is available
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)
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```
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## Built-in Tools
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CrewAI ships tool packages: `crewai-tools` with SerperDevTool, ScrapeWebsiteTool, etc. Install separately:
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```bash
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pip install crewai-tools
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```
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## Tool Design Guidelines
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- **Docstring matters.** The docstring/description is what the LLM sees to decide when to use the tool.
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- **Type hints required.** Tool parameters use type hints for schema generation.
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- **Handle errors gracefully.** Tool failures mark tasks as failed but don't raise exceptions.
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- **Return strings.** Keep return values as strings for consistent handling.
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- **Cache results.** Same input → same cached output (controlled by `cache` parameter).
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